Papers by Peter Wu
Connecting Language and Vision to Actions (P18-5)
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| Challenge: | Recent advances in language and vision have made incredible progress in describing images and interacting with visual content in a physical or embodied environment. |
| Approach: | This tutorial will provide an overview of the growing number of multimodal tasks and datasets that combine textual and visual understanding. |
| Outcome: | This tutorial will review the state-of-the-art approaches to selected tasks such as image captioning, visual question answering and visual dialog. |
Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts (2025.emnlp-main)
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Georgios Chochlakis, Peter Wu, Tikka Arjun Singh Bedi, Marcus Ma, Kristina Lerman, Shrikanth Narayanan
| Challenge: | Existing approaches to model complex subjective tasks in natural language are limited by significant variation in annotations. |
| Approach: | They propose a simple in-context learning binary filtering baseline that estimates the reasonableness of a document-label pair. |
| Outcome: | The proposed approach can be integrated into annotation pipelines to enhance signal-to-noise ratios. |
LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning (2024.findings-emnlp)
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| Challenge: | Existing methods require human experts or pre-trained LLMs to describe the skill to guide the selection. |
| Approach: | They propose a new approach that uses unsupervised learning to create a latent space representation of rationales with a variable called a reasoning skill. |
| Outcome: | Empirical results show that LaRS outperforms SOTA skill-based selection methods . it processes example banks four times faster and reduces LLM inferences by half . |
We’re Afraid Language Models Aren’t Modeling Ambiguity (2023.emnlp-main)
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Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, Yejin Choi
| Challenge: | Ambiguity is an intrinsic feature of natural language, allowing us to anticipate misunderstandings and revise our interpretations as listeners. |
| Approach: | They use AmbiEnt to capture ambiguity in a sentence and analyze it to evaluate pretrained LMs. |
| Outcome: | The proposed model can flag political claims in the wild that are misleading due to ambiguity. |